Assessing the Effectiveness of Outlier Detection in Protecting Statistical Models from Data Anomalies

Posted: Jan 29, 2012

• Madison Kelly
• Makayla Ross
• Marcus Ward

Abstract

The proliferation of statistical models across critical domains including healthcare diagnostics, financial forecasting, and autonomous systems has elevated the importance of robust model performance in the presence of data anomalies. While outlier detection methods are commonly employed as a preprocessing step to safeguard model integrity, their effectiveness remains inadequately quantified across diverse anomaly types and model architectures. This research introduces a novel evaluation framework that systematically assesses the protective efficacy of outlier detection algorithms against a comprehensive taxonomy of data anomalies. We propose a multi-dimensional classification of anomalies that extends beyond traditional point outliers to include contextual, collective, and adversarial anomalies, each presenting distinct challenges to statistical models. Our methodology employs a cross-domain experimental design incorporating real-world datasets from healthcare, finance, and sensor networks, augmented with synthetically generated anomalies following carefully designed contamination patterns. We evaluate twelve outlier detection algorithms spanning statistical, distance-based, density-based, and machine learning approaches against five representative statistical models including linear regression, random forests, gradient boosting, neural networks, and support vector machines. Results reveal significant variation in protective efficacy, with ensemble-based detection methods demonstrating superior performance against contextual anomalies while showing vulnerability to carefully crafted adversarial outliers. Surprisingly, we find that in approximately 23% of experimental conditions, the application of outlier detection actually degraded model performance compared to training on contaminated data, particularly when anomaly characteristics aligned with legitimate data patterns in high-dimensional spaces. Our findings challenge the conventional wisdom that outlier detection universally enhances model robustness and provide actionable insights for selecting appropriate detection strategies based on anomaly type, data domain, and model characteristics. This research contributes a rigorous evaluation methodology and evidence-based guidelines for deploying outlier detection as a protective mechanism for statistical models.